A5052818910- Neural Networks Stability and Synchronization
- Stability and Control of Uncertain Systems
- Power Systems and Renewable Energy
- Advanced Memory and Neural Computing
- Microgrid Control and Optimization
- Distributed Control Multi-Agent Systems
- Advanced Battery Technologies Research
- Frequency Control in Power Systems
- Neural Networks and Applications
- HVDC Systems and Fault Protection
- Matrix Theory and Algorithms
Northeastern University
2024-2025
Shanghai University
2022
Tiangong University
2016-2018
This article addresses the problems of robustly exponential stability and stabilization for uncertain linear discrete-time periodic systems with time delay in state variables polytopic-type parameter uncertainty. By constructing novel uncertainty-dependent Lyapunov–Krasovskii functionals, we establish some sufficient conditions forms matrix inequalities, which guarantee system is exponentially stable. Then, by utilizing static feedback free weighting technique, give to ensure delay. Finally,...
This paper is concerned with finite-time stability for a class of neutral neural networks time-varying delay and norm-bounded uncertainties. Delay-dependent sufficient conditions, which guarantee that the uncertain network stable, are proposed in terms linear matrix inequalities by Lyapunov-Krasovskii functional method. Finally, numerical example given to show effectiveness benefits result.
This paper is concerned with the exponential [Formula: see text] stabilization for a class of uncertain neural networks interval time-varying delay and external disturbance via periodically intermittent control. By constructing novel Lyapunov–Krasovskii functional (LKF) applying some inequality techniques, delay-dependent sufficient conditions are derived to guarantee considered closed-loop system. These given in form linear matrix inequalities (LMIs). The state-feedback controller can...